Attribute factor analysis method, device, and program

ABSTRACT

This invention relates to a method of analyzing a factor of an attribute based on a case sample set containing combinations of image data and attribute data associated with the image data. The attribute factor analysis method includes: a division step of dividing an image region of the image data forming each element of the case sample set into parts in a mesh shape of a predetermined sample size; a reconstruction step of reconstructing, based on the case sample set, the case sample sets for the respective parts to obtain reconstructed case sample sets; an analysis step of analyzing, for each of the reconstructed case sample sets, a dependency between an explanatory variable representing a feature value of image data on each part and an objective variable representing the attribute data, to thereby obtain an attribute factor analysis result; and a visualization step of visualizing the attribute factor analysis result to produce the visualized attribute factor analysis result.

TECHNICAL FIELD

This invention relates to a method of estimating an attribute of anobject, and more particularly, to a method, a device, and a program foranalyzing a factor of an attribute.

BACKGROUND ART

There is known “supervised learning” as a technique of machine learning(for example, refer to Patent Literature 1). In supervised learning, acase data set containing combinations of input data (observed data) andoutput data (implication, attribute, or result of observed data) isregarded as “advice from a supervisor”, and a machine (computer) learnsbased on the case data set. The phrase “learning” in this context meanscreating a function model for predicting or estimating output for inputdata whose output is unknown.

Next, a specific description is given taking facial image recognition asan example. In this facial image recognition, a description is given ofa case in which sex (one of human attributes) is estimated based on afacial image.

At the time of learning, a computer constructs a function model based ona case data set containing facial images of females and males. At thetime of evaluation, when a facial image (for example, female facialimage) whose sex is unknown is supplied, the computer produces “female”as its sex based on the input data and the function model.

As a method of calculating a magnitude of a correlation between anexplanatory variable representing a feature value of an object and anobjective variable representing an attribute or a result, there areknown, for example, a method of calculating a correlation value in asub-space (one-dimension) of canonical correlation analysis (CCA),maximum likelihood mutual information (MLMI), which is a method ofcalculating mutual information (MI) (for example, see Non PatentLiterature 1), or least-squares mutual information (LSMI), which is amethod of calculating squared-loss mutual information (SMI) (forexample, see Non Patent Literature 2).

CITATION LIST Patent Literature

-   Patent Literature 1: JP-A-H11-175724

Non Patent Literature

-   Non Patent Literature 1: Suzuki, T., Sugiyama, M., Sese, J., &    Kanamori, T. “Approximating Mutual Information by Maximum Likelihood    Density Ratio Estimation” In Y. Saeys, H. Liu, I. Lnza, L. Wehenkel,    and Y. Van de Peer (Eds.), Proceedings of the Workshop on New    Challenges for Feature Selection in Data Mining and Knowledge    Discovery 2008 (FSDM2008), JMLR Workshop and Conference Proceeding,    vol. 4, pp. 5-20, 2008-   Non Patent Literature 2: Suzuki, T., Sugiyama, M., Kanamori, T., &    Sese, J. “Mutual Information Estimation Reveals Global Associations    between Stimuli and Biological Processes” BMC Bioinformatics, vol.    10, no. 1, pp. S52, 2009

SUMMARY OF INVENTION Technical Problem

In the related-art supervised learning described above, output (e.g.,sex) can only be estimated based on the input data (facial image).

In the related art, when a set {(x_(i), y_(i))} of case data (a pair ofthe feature value and the attribute of an object) is given, there is notechnology for analyzing an overall tendency of “which explanatoryvariable X representing the feature value of an object tends to be afactor of an objective variable Y representing an attribute or aresult”, and visualizing the result.

First, a first example will be described. A database of male and femalefacial images is constructed, but there is no technology for analyzingan overall tendency of which facial part tends to be a decisive factorof sex. In this context, the phrase “which facial part tends to be adecisive factor of sex” means which facial part has an influence on“masculinity” or “femininity”. In other words, what determines thefeature of males and females is unknown.

Next, a second example will be described. Impressions of “prettiness”are evaluated for a database of female facial images, but there is notechnology for analyzing an overall tendency of which facial part is adecisive factor of the attribute (impression of “prettiness”). In otherwords, what point determines “prettiness” is unknown.

It is an object of this invention to provide an attribute factoranalysis method, a device, and a program, which are capable of analyzinga factor of an attribute.

Solution to Problem

A mode of the present invention is an attribute factor analysis method,which is a method of analyzing a factor of an attribute with use of anattribute factor analysis device based on a case sample set containingcombinations of image data and attribute data associated with the imagedata, the attribute factor analysis method comprising a division step ofdividing an image region of the image data forming each element of thecase sample set into parts in a mesh shape of a predetermined samplesize, a reconstruction step of reconstructing, based on the case sampleset, the case sample sets for the respective parts to obtainreconstructed case sample sets, an analysis step of analyzing, for eachof the reconstructed case sample sets, a dependency between anexplanatory variable representing a feature value of image data on eachpart and an objective variable representing the attribute data, tothereby obtain an attribute factor analysis result, and a visualizationstep of visualizing the attribute factor analysis result to produce thevisualized attribute factor analysis result.

Advantageous Effects of Invention

According to this invention, it is possible to analyze a factor of anattribute.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram for illustrating a configuration of anattribute factor analysis device according to a first embodiment of thisinvention;

FIG. 2 shows diagrams for illustrating one example of a case samplerelating to a facial image and examples of a reconstructed case sample;

FIG. 3 shows diagrams each for illustrating an example of an attributefactor analysis result that is visualized by a visualization processingunit of FIG. 1;

FIG. 4 is a block diagram for illustrating a configuration of anattribute factor analysis device according to a second embodiment ofthis invention;

FIG. 5 is a diagram for illustrating adjustment of a sample size;

FIG. 6 is a block diagram for illustrating a configuration of anattribute factor analysis device according to the second embodiment ofthis invention;

FIG. 7 shows diagrams for illustrating a division position in theattribute factor analysis device illustrated in FIG. 1; and

FIG. 8 shows diagrams for illustrating adjustment of a division positionin the attribute factor analysis device illustrated in FIG. 6.

DESCRIPTION OF EMBODIMENTS First Embodiment

FIG. 1 is a block diagram for illustrating a configuration of anattribute factor analysis device 100 according to a first embodiment ofthis invention. A description is simplified or omitted for aconfiguration having low relevance to this invention.

The illustrated attribute factor analysis device 100 can be realized bya computer configured to operate in accordance with program control. Theattribute factor analysis device 100 comprises an input device 10configured to input data, a data processing device 20, an output device30 configured to produce a processing result of the data processingdevice 20, and an auxiliary storage device 40 configured to function asvarious kinds of databases.

Although not shown, the data processing device 20 comprises a read-onlymemory (ROM) storing a program, a random-access memory (RAM) to be usedas a working memory temporarily storing data, and a central processingunit (CPU) configured to process data stored in the RAM in accordancewith the program stored in the ROM.

The auxiliary storage device 40 is configured to accumulate a casesample set. The case sample set is a set containing combinations ofimage data and attribute data associated with this image data. In thisexample, the number of samples is, for example, 2,600. It is to beunderstood that the number of samples is not limited thereto.

Further, in the illustrated example, the image data is facial imagedata. Further, the attribute data is data representing impressions ofappearances of faces.

The image data is not necessarily facial image data, but normalizationprocessing is performed on all the image data. In this context, thenormalization processing means performing positioning at a particularpart. For example, when the image data is facial image data, thenormalization processing means performing positioning at both eyes.

The illustrated attribute factor analysis device 100 is a deviceconfigured to analyze whether or not there is a correlation between thefeature of face-part data and an impression of an appearance.

The input device 10 is configured to supply a case sample set stored inthe auxiliary storage device 40 into the data processing device 20.

The data processing device 20 comprises a part division processing unit22, a case sample set reconstruction processing unit 24, a dependencyanalysis processing unit 26, and a visualization processing unit 28.

The part division processing unit 22 is configured to divide an imageregion of image data contained in a case sample set into parts in a meshshape of a part of a predetermined sample size.

The case sample set reconstruction processing unit 24 is configured toreconstruct, based on the case sample set, case sample sets forrespective parts, to obtain reconstructed case sample set.

The dependency analysis processing unit 26 is configured to analyze, foreach of the reconstructed case sample sets, a dependency between anexplanatory variable X representing a feature value of image data ofeach part and an objective variable Y representing attribute data, toobtain an attribute factor analysis result.

The visualization processing unit 28 is configured to visualize theattribute factor analysis result to produce the visualized attributefactor analysis result to the output device 30.

Next, a description will be given in detail of an operation of eachprocessing unit of the data processing device 20.

FIG. 2(A) is a diagram for illustrating one example of a case samplerelating to a facial image. The case sample is a combination of theexplanatory variable X representing a feature value of facial image dataand the objective variable Y representing an attribute “impression” of aface. As described above, in the case of this example, 2,600 casesamples of this kind are accumulated in the auxiliary storage device 40.

The part division processing unit 22 determines a part division method(mesh division method) of the explanatory variable X in accordance withthe purpose of analysis, customer needs, and the like. In this example,the part division processing unit 22 first normalizes the facial imagedata at a position of both eyes for all the 2,600 facial image samples.In this example, one piece of image data is (64×64) pixels.

Next, the part division processing unit 22 divides the normalized facialimage data into pieces of block image data of (8×8) pixels. Thus, in thecase of this example, the part division processing unit 22 obtains 64pieces of block image data as illustrated in FIG. 2(A).

In this case, an elaborated part division technique needs to be used.This point will be described later.

The case sample set reconstruction processing unit 24 reconstructs,based on the case sample set, case sample sets for respective parts thatare determined by the part division processing unit 22.

FIG. 2(B) and FIG. 2(C) are each a diagram for illustrating an exampleof a reconstructed case sample. FIG. 2(B) is an illustration of a casesample reconstructed for a right-eye image, and FIG. 2(C) is anillustration of a case sample reconstructed for a left-half mouth image.

As illustrated in FIG. 2(B), the case sample reconstructed for aright-eye image contains a combination of an explanatory variable X1representing a feature value of the right-eye image and an objectivevariable Y representing an attribute “impression” of that value. In thisexample, the objective variable Y illustrated in FIG. 2(A) is used(appropriated) as it is as the attribute “impression”. The number ofcase samples is 2,600.

As illustrated in FIG. 2(C), the case sample reconstructed for aleft-half mouth image contains a combination of an explanatory variableX2 representing a feature value of the left-half mouth image and theobjective variable Y representing an attribute “impression” of thatvalue. The number of those case samples is also 2,600.

The feature value of image data is any selected one of feature valuesincluding RGB, gray scale, Laplacian, and Haar-like feature value.Further, each part uses a common feature value.

Further, in the first embodiment, four kinds of feature values of imagedata are given, but this invention is not limited thereto. It is to beunderstood that other feature values may be used.

The dependency analysis processing unit 26 analyzes a correlation(magnitude of correlation) between the explanatory variable X and theobjective variable Y for each of the case sample sets (see FIG. 2(B) andFIG. 2(C)) reconstructed by the case sample set reconstructionprocessing unit 24.

In this example, as a method of calculating the magnitude of thecorrelation between the explanatory variable X and the objectivevariable Y, the dependency analysis processing unit 26 uses any one of amethod of calculating a correlation value (correlation ratio) in asub-space (one-dimension) of canonical correlation analysis (CCA), amethod of calculating mutual information, and a method of calculatingsquared-loss mutual information. As described above, the method ofcalculating mutual information is disclosed in Non Patent Literature 1,and the method of calculating squared-loss mutual information isdisclosed in Non Patent Literature 2.

In this example, the dependency analysis processing unit 26 uses thesame technique common to respective parts.

Next, an outline of mutual information will be described.

It will be assumed that p(x) is a probability function of x, and p(y) isa probability function of y. In addition, it will be assumed that p(x,y)is a joint probability function of x and y.

It is assumed that x and y are independent of each other. In otherwords, it is assumed that a function of y=f(x) cannot be obtained. Inthis case, Expression (1) is satisfied.

p(x)p(x)=p(x,y)  (1)

In contrast, it is assumed that x and y are not independent of eachother. In other words, it is assumed that a function of y=f(x) can beobtained. In this case, Expression (2) is satisfied.

$\begin{matrix}{{I\left( {X;Y} \right)} = {{\int_{Y}{\int_{X}{{p\left( {x,y} \right)}\log \frac{p\left( {x,y} \right)}{{p(x)}{p(y)}}\ {x}\ {y}}}} > 0}} & (2)\end{matrix}$

The mutual information is represented by Expression (3).

$\begin{matrix}{{I\left( {X;Y} \right)} = {\int_{Y}{\int_{X}{{p\left( {x,y} \right)}\log \frac{p\left( {x,y} \right)}{{p(x)}{p(y)}}\ {x}\ {y}}}}} & (3)\end{matrix}$

In the first embodiment, three kinds of methods of calculating themagnitude of the correlation between the explanatory variable X and theobjective variable Y are given, but this invention is not limitedthereto. It is to be understood that other calculation methods may beused.

The visualization processing unit 28 visualizes the attribute factoranalysis result produced by the dependency analysis processing unit 26,and produces the visualized attribute factor analysis result to theoutput device 30. In this case, the attribute factor analysis resultrefers to a region extracted as a factor of an attribute.

To be more specific, the visualization processing unit 28 visualizes theabsolute value of a correlation value, the magnitude of a numericalvalue of mutual information, or the magnitude of a numerical value ofsquared-loss mutual information for each block image (part), which iscalculated by the dependency analysis processing unit 26, in a matrix(color-matrix) form as illustrated in FIG. 3.

In FIG. 3, as the attribute (impression), an example of five kinds ofattributes, namely, “attribute 1”, “attribute 2”, “attribute 3”,“attribute 4”, and “attribute 5”, is illustrated.

FIG. 3(A) is an illustration of an example of visualization of theattribute factor analysis result when the impression of the attribute 1is “cheerfulness and friendliness”. FIG. 3(B) is an illustration of anexample of visualization of the attribute factor analysis result whenthe impression of the attribute 2 is “prettiness”. FIG. 3(C) is anillustration of an example of visualization of the attribute factoranalysis result when the impression of the attribute 3 is “businessappropriateness”. FIG. 3(D) is an illustration of an example ofvisualization of the attribute factor analysis result when theimpression of the attribute 4 is “kindness”. FIG. 3(E) is anillustration of an example of visualization of the attribute factoranalysis result when the impression of the attribute 5 is “healthyappearance”. The phrase “business appropriateness” means an impressionof a person when he or she works in an office building.

As illustrated in FIG. 3(A) to FIG. 3(E), as the numerical valueindicating the magnitude of a correlation becomes larger, the colorbecomes stronger (colored to a deep red). Further, through visualizationof the attribute factor analysis result, it is possible to explain adifference in magnitude of a correlation for each part in aneasy-to-understand manner.

For example, in the case of the impression “cheerfulness andfriendliness” as illustrated in FIG. 3(A), the attribute factor analysisresult indicates that the correlation is large in the vicinity of themouth, cheeks, and eyes. In other words, it is indicated that thevicinity of the mouth, cheeks, and eyes is a decisive factor of theimpression “cheerfulness and friendliness”. Further, in the case of theimpression “prettiness” as illustrated in FIG. 3(B), the attributefactor analysis result indicates that the correlation is large in thevicinity of the forehead and the chin. In other words, it is indicatedthat the vicinity of the forehead and the chin is a decisive factor ofthe impression “prettiness”.

In the case of the impression “business appropriateness” as illustratedin FIG. 3(C), the attribute factor analysis result indicates that thecorrelation is large in the vicinity of the hair region. In other words,it is indicated that the vicinity of the hair region is a decisivefactor of the impression “business appropriateness”.

In the case of the impression “kindness” as illustrated in FIG. 3(D),the attribute factor analysis result indicates that the correlation islarge in the vicinity of the mouth, eyes, and cheeks. In other words, itis indicated that the vicinity of the mouth, eyes, and cheeks is adecisive factor of the impression “kindness”.

In the case of the impression “healthy appearance” as illustrated inFIG. 3(E), the attribute factor analysis result indicates that thecorrelation is large in the vicinity of the mouth, eyes, and cheeks. Inother words, it is indicated that the vicinity of the mouth, eyes, andcheeks is a decisive factor of the impression “healthy appearance”.

The following points are understood as an overall tendency based on FIG.3(A) to FIG. 3(E).

First, it is understood that some specific facial parts and theimpression have a high correlation. To be more specific, it isunderstood that, aside from the magnitude of a correlation value, partshaving a high correlation with a particular impression are almost commonto each other (e.g., eyes and mouth) irrespective of the kind of featurevalue of a face or an analysis technique (CCA, MLMI, and LSMI).

Next, the position of a facial part having a high correlation isdifferent depending on the kind of impression.

In the first embodiment, five kinds of impressions of a face are given,but this invention is not limited thereto. It is to be understood thatat least one impression may be selected from those five kinds ofimpressions, or another impression may be used.

The respective units of the attribute factor analysis device 100according to the first embodiment may be implemented by using acombination of hardware and software. In an embodiment employing acombination of hardware and software, the respective units areimplemented as various kinds of means by operating a piece of hardware,e.g., a control unit (CPU), based on an attribute factor analysisprogram stored in the ROM. Further, the attribute factor analysisprogram may be recorded in a recording medium for distribution. Theattribute factor analysis program recorded in the recording medium isread into a memory in a wired or wireless manner, or via the recordingmedium itself, to thereby operate the control unit and other components.Examples of the recording medium include an optical disc, a magneticdisk, a semiconductor memory device, and a hard disk.

The attribute factor analysis device 100 configured in this manneraccording to the first embodiment can analyze the factor of an attribute(impression).

Second Embodiment

FIG. 4 is a block diagram for illustrating a configuration of anattribute factor analysis device 100A according to a second embodimentof this invention. A description is simplified or omitted for aconfiguration having low relevance to this invention.

The illustrated attribute factor analysis device 100A can be realized bya computer configured to operate in accordance with program control. Theattribute factor analysis device 100A has the same configuration as theattribute factor analysis device 100 illustrated in FIG. 1 for operationexcept that the configuration of the data processing device is differentas described later. Thus, the data processing device is denoted by areference numeral 20A. The same components as those illustrated in FIG.1 are denoted by the same reference numerals, and a description thereofis omitted for the sake of simplicity of description.

The illustrated data processing device 20A has the same configuration asthe data processing device 20 illustrated in FIG. 1 for operation exceptthat the configuration of the part division processing unit is differentas described later. Thus, the part division processing unit is denotedby a reference numeral 22A.

In addition to performing the part division processing by the partdivision processing unit 22 illustrated in FIG. 1, the part divisionprocessing unit 22A further includes a sample size adjustment unit 222configured to previously calculate the magnitude of the correlationbetween the explanatory variable X and the objective variable Y for eachpiece of part data, and to adjust a sample size.

Next, an operation of the sample size adjustment unit 222 will bedescribed in more detail.

As illustrated in FIG. 5, the magnitude of a sample size (in the case ofFIG. 5, the size of block image data) needs to be determined in advanceappropriately. In the case of FIG. 5, each piece of block image data is(8×8) pixels. Further, the image region of image data is divided intoparts in a mesh shape of (8×8) pixels. That is, each piece of image datais divided into 64 pieces of block image data.

When the sample size is too small, the magnitudes of the correlationcannot be compared. That is, the correlation with the objective variableY is extremely small for every piece of part data.

In contrast, when the sample size is too large, the original purpose ofanalysis cannot be achieved. In other words, it is difficult to identifya part having a high relevance to the objective variable Y.

In view of this, the sample size adjustment unit 222 previouslycalculates the magnitude of the correlation between the explanatoryvariable X and the objective variable Y for each piece of part datawhile appropriately changing the sample size, and specifies anappropriate sample size while adjusting a balance between “variance ofcorrelation values for respective parts (the larger the better)” and“size of a part to be analyzed (the smaller the better)”.

The respective units of the attribute factor analysis device 100Aaccording to the second embodiment may be implemented by using acombination of hardware and software. In the embodiment employing acombination of hardware and software, the respective units areimplemented as various kinds of means by operating a piece of hardware,e.g., a control unit (CPU), based on an attribute factor analysisprogram stored in the ROM. Further, the attribute factor analysisprogram may be recorded in a recording medium for distribution. Theattribute factor analysis program recorded in the recording medium isread into a memory in a wired or wireless manner, or via the recordingmedium itself, to thereby operate the control unit and other components.Examples of the recording medium include an optical disc, a magneticdisk, a semiconductor memory device, and a hard disk.

The attribute factor analysis device 100A configured in this manneraccording to the second embodiment can easily analyze the factor of anattribute (impression).

Third Embodiment

FIG. 6 is a block diagram for illustrating a configuration of anattribute factor analysis device 100B according to a third embodiment ofthis invention. A description is simplified or omitted for aconfiguration having low relevance to this invention.

The illustrated attribute factor analysis device 100B can be realized bya computer configured to operate in accordance with program control. Theattribute factor analysis device 100B has the same configuration as theattribute factor analysis device 100 illustrated in FIG. 1 for operationexcept that the configuration of the data processing device is differentas described later. Thus, the data processing device is denoted by areference numeral 20B. The same components as those illustrated in FIG.1 are denoted by the same reference numerals, and a description thereofis omitted for the sake of simplicity of description.

The illustrated data processing device 20B includes a part divisionprocessing unit 22B, a case sample set reconstruction processing unit24B, a dependency analysis processing unit 26B, and a visualizationprocessing unit 28B.

The part division processing unit 22B is configured to divide the imageregion of image data into parts of a predetermined sample size whileshifting the division position thereof by a predetermined number ofpixels.

The case sample set reconstruction unit 24B is configured to obtain acase sample set reconstructed as described above for each of the shiftedparts.

The dependency analysis processing unit 26B is configured to perform theanalysis of dependency in parallel for each of the shifted parts, and toobtain a plurality of attribute factor analysis results.

The visualization processing unit 28B is configured to integrate(average) the plurality of attribute factor analysis results, visualizethe integrated attribute factor analysis result, and produce thevisualized attribute factor analysis result to the output device 30.

In this manner, the attribute factor analysis device 100B according tothe third embodiment adjusts the division position.

Next, adjustment of this division position will be described in detailin comparison to the attribute factor analysis device 100 illustrated inFIG. 1.

FIG. 7 are diagrams for illustrating a division position in theattribute factor analysis device 100 illustrated in FIG. 1. FIG. 7(A) isan illustration of pieces of image data divided by the part divisionprocessing unit 22. FIG. 7(B) is an illustration of an attribute factoranalysis result (visualized by the visualization processing unit 28)obtained by the dependency analysis processing unit 26.

As described above, the division position of a part is not alwayslocated in an appropriate position.

In the case of FIG. 7(A), a division line passes through a center lineof the eyebrows, the nose, and the mouth. Thus, as illustrated in FIG.7(B), it is difficult to identify the magnitudes of a correlation withthe objective variable Y for pieces of part data, namely, the entireeyebrows, the entire nose, and the entire mouth.

FIG. 8 are diagrams for illustrating adjustment of a division positionin the attribute factor analysis device 100B illustrated in FIG. 6. FIG.8(A) is an illustration of a plurality of divided pieces of image data,which are obtained by the part division processing unit 22B shifting thedivision position. FIG. 8(B) is an illustration of a plurality ofattribute factor analysis results, which are obtained by the dependencyanalysis processing unit 26B performing the analysis of dependency inparallel. FIG. 8(C) is an illustration of an integrated attribute factoranalysis result visualized by the visualization processing unit 28B.

As illustrated in FIG. 8(A), the part division processing unit 22Bdivides the image region of image data into parts of a predeterminedsample size while shifting the division position thereof by a number ofpixels (e.g., 2 pixels).

The case sample set reconstruction processing unit 24B obtains a casesample set reconstructed as described above for each of the shiftedparts.

As illustrated in FIG. 8(B), the dependency analysis processing unit 26Bperforms the analysis of dependency in parallel for each of the shiftedparts, and obtains a plurality of attribute factor analysis results.

As illustrated in FIG. 8(C), the visualization processing unit 28Bintegrates (averages pixels at the same position) the plurality ofattribute factor analysis results (color matrix), visualizes theintegrated attribute factor analysis result, and produces the visualizedattribute factor analysis result to the output device 30.

In this manner, through integration of the plurality of attribute factoranalysis results (color matrix), it is possible to present acomprehensible integrated (averaged) attribute factor analysis result(color matrix) that exhibits a gradient in display (red display).

The respective units of the attribute factor analysis device 100Baccording to the third embodiment may be implemented by using acombination of hardware and software. In the embodiment employing acombination of hardware and software, the respective units areimplemented as various kinds of means by operating a piece of hardware,e.g., a control unit (CPU), based on an attribute factor analysisprogram stored in the ROM. Further, the attribute factor analysisprogram may be recorded in a recording medium for distribution. Theattribute factor analysis program recorded in the recording medium isread into a memory in a wired or wireless manner, or via the recordingmedium itself, to thereby operate the control unit and other components.Examples of the recording medium include an optical disc, a magneticdisk, a semiconductor memory device, and a hard disk.

The attribute factor analysis device 100B configured in this manneraccording to the third embodiment can analyze the factor of an attribute(impression) in an easy-to-understand manner.

In the embodiments described above, processing of the embodiments may beexecuted by installing on a computer information stored in acomputer-readable storage medium, which is coded with an instructionexecutable by a program, software, or a computer. The storage mediumincludes a transmission medium configured to temporarily record and holddata, e.g., a network, in addition to a portable recording medium suchas an optical disc, a floppy (trademark) disk, and a hard disk.

Modified Example

While the invention has been particularly shown and described withreference to exemplary embodiments thereof, the invention is not limitedto those embodiments. It will be understood by ordinary skill in the artthat various changes in form and details may be made therein withoutdeparting from the spirit and scope of the present invention as definedby the claims.

For example, in the embodiments described above, a description is givenwith an example of a case in which the image region of each piece ofimage data is a square, but the image region of each piece of image datamay be a rectangle.

Further, in the embodiments described above, the square image region ofeach piece of image data is divided into 64 pieces of square block imagedata of 8×8 pixels, but it is to be understood that the shape of blockimage data and the number of divisions are not limited thereto. Forexample, in general, a rectangle image region of each piece of imagedata may be divided into (M×N) pieces of, namely, M-by-N rectangle blockimage data. In this case, M and N are first and second integers of 2 ormore, respectively. It is preferred that the first integer M and thesecond integer N be each 6 or more because too large a sample sizeinhibits the original purpose of analysis from being achieved.

Further, in the embodiments described above, a plurality of dividedpieces of block image data have the same sample size, but it is to beunderstood that the sample sizes may be different from one another.Specifically, the image region of each piece of image data may bedivided into a plurality of pieces of block image data of differentsample sizes so that the division line does not pass through acharacteristic part (e.g., mouth, eyes, and nose) of a face.

Further, in the embodiments described above, a description is given withan example of a case in which the image data is face image data and theattribute is an impression of a face, but it is to be understood thatthis invention is not limited thereto. The image data may be image dataother than the facial image data, and the attribute may be an attributeother than the impression.

INDUSTRIAL APPLICABILITY

This invention can be applied to, for example, a make-up simulation orgiving flexible advice on make-up improvement in accordance with theintention (e.g., an ideal image) of a customer.

REFERENCE SIGNS LIST

-   10 input device-   20, 20A, 20B data processing device-   22, 22A, 22B part division processing unit-   222 sample size adjustment unit-   24, 24B case sample set reconstruction processing unit-   26, 26B dependency analysis processing unit-   28, 28B visualization processing unit-   30 output device-   40 auxiliary storage device-   100, 100A, 100B attribute factor analysis device

What is claimed is:
 1. An attribute factor analysis method, which is amethod of analyzing a factor of an attribute with use of an attributefactor analysis device based on a case sample set containingcombinations of image data and attribute data associated with the imagedata, the attribute factor analysis method comprising: dividing an imageregion of the image data forming each element of the case sample setinto parts in a mesh shape of a predetermined sample size;reconstructing, based on the case sample set, the case sample sets forthe respective parts to obtain reconstructed case sample sets;analyzing, for each of the reconstructed case sample sets, a dependencybetween an explanatory variable representing a feature value of imagedata on each part and an objective variable representing the attributedata, to thereby obtain an attribute factor analysis result; andvisualizing the attribute factor analysis result to produce thevisualized attribute factor analysis result.
 2. The attribute factoranalysis method according to claim 1, wherein the dividing comprisesadjusting the predetermined sample size by previously calculating amagnitude of a correlation between the explanatory variable and theobjective variable for the image data on each part.
 3. The attributefactor analysis method according to claim 1, wherein the dividingcomprises dividing the image region of the image data into the parts ofa predetermined sample size while shifting a division position of theimage region of the image data by a predetermined number of pixels,wherein the reconstructing comprises obtaining the reconstructed casesample sets for the respective shifted parts, wherein the analyzingcomprises obtaining a plurality of attribute factor analysis results byanalyzing the dependency in parallel for the respective shifted parts,and wherein the visualizing comprises integrating the plurality ofattribute factor analysis results and visualizing the integratedattribute factor analysis result to produce the visualized attributefactor analysis result.
 4. The attribute factor analysis methodaccording to claim 1, wherein the feature value of image data comprisesany selected one of feature values comprising RGB, gray scale,Laplacian, and Haar-like feature value.
 5. The attribute factor analysismethod according to claim 1, wherein the analyzing comprises analyzingthe dependency by any selected one of calculating a correlation value ina sub-space of canonical correlation analysis, calculating mutualinformation, and calculating squared-loss mutual information.
 6. Theattribute factor analysis method according to claim 1, wherein the imagedata comprises facial image data.
 7. The attribute factor analysismethod according to claim 6, wherein the attribute data comprises datarepresenting an impression of a face.
 8. The attribute factor analysismethod according to claim 7, wherein the impression comprises at leastone selected from among impressions comprising “cheerfulness andfriendliness”, “prettiness”, “business appropriateness”, “kindness”, and“healthy appearance”.
 9. An attribute factor analysis device, which isconfigured to analyze a factor of an attribute based on a case sampleset containing combinations of image data and attribute data associatedwith the image data, the attribute factor analysis device comprising: apart division processing unit configured to divide an image region ofthe image data forming each element of the case sample set into parts ina mesh shape of a predetermined sample size; a case sample setreconstruction processing unit configured to reconstruct, based on thecase sample set, the case sample sets for the respective parts to obtainreconstructed case sample sets; a dependency analysis processing unitconfigured to analyze, for each of the reconstructed case sample sets, adependency between an explanatory variable representing a feature valueof image data on each part and an objective variable representing theattribute data, to thereby obtain an attribute factor analysis result;and a visualization processing unit configured to visualize theattribute factor analysis result to produce the visualized attributefactor analysis result.
 10. The attribute factor analysis deviceaccording to claim 9, wherein the part division processing unitcomprises a sample size adjustment unit configured to adjust thepredetermined sample size by previously calculating a magnitude of acorrelation between the explanatory variable and the objective variablefor the image data on each part.
 11. The attribute factor analysisdevice according to claim 9, wherein the part division processing unitis configured to divide the image region of the image data into theparts of a predetermined sample size while shifting a division positionof the image region of the image data by a predetermined number ofpixels, wherein the case sample set reconstruction processing unit isconfigured to obtain the reconstructed case sample sets for therespective shifted parts, wherein the dependency analysis processingunit is configured to obtain a plurality of attribute factor analysisresults by analyzing the dependency in parallel for the respectiveshifted parts, and wherein the visualization processing unit isconfigured to integrate the plurality of attribute factor analysisresults, to visualize the integrated attribute factor analysis result,and to produce the visualized attribute factor analysis result.
 12. Theattribute factor analysis device according to claim 9, wherein thefeature value of image data comprises any selected one of feature valuescomprising RGB, gray scale, Laplacian, and Haar-like feature value. 13.The attribute factor analysis device according to claim 9, wherein thedependency analysis processing unit is configured to analyze thedependency by any selected one of calculating a correlation value in asub-space of canonical correlation analysis, calculating mutualinformation, and calculating squared-loss mutual information.
 14. Theattribute factor analysis device according to claim 9, wherein the imagedata comprises facial image data.
 15. The attribute factor analysisdevice according to claim 14, wherein the attribute data comprises datarepresenting an impression of a face.
 16. The attribute factor analysisdevice according to claim 15, wherein the impression comprises at leastone selected from among impressions comprising “cheerfulness andfriendliness”, “prettiness”, “business appropriateness”, “kindness”, and“healthy appearance”.
 17. A non-transitory computer readable recordingmedium for storing an attribute factor analysis program for causing acomputer to analyze a factor of an attribute based on a case sample setcontaining combinations of image data and attribute data associated withthe image data, the attribute factor analysis program causing thecomputer to execute: a division procedure of dividing an image region ofthe image data forming each element of the case sample set into parts ina mesh shape of a predetermined sample size; a reconstruction procedureof reconstructing, based on the case sample set, the case sample setsfor the respective parts to obtain reconstructed case sample sets; ananalysis procedure of analyzing, for each of the reconstructed casesample sets, a dependency between an explanatory variable representing afeature value of image data on each part and an objective variablerepresenting the attribute data, to thereby obtain an attribute factoranalysis result; and a visualization procedure of visualizing theattribute factor analysis result to produce the visualized attributefactor analysis result.
 18. The non-transitory computer readablerecording medium according to claim 17, wherein the division procedurecomprises further causing the computer to execute an adjustmentprocedure of adjusting the predetermined sample size by previouslycalculating a magnitude of a correlation between the explanatoryvariable and the objective variable for the image data on each part. 19.The non-transitory computer readable recording medium according to claim17, the division procedure comprises causing the computer to divide theimage region of the image data into the parts of the predeterminedsample size while shifting a division position of the image region ofthe image data by a predetermined number of pixels, the reconstructionprocedure comprises causing the computer to obtain the reconstructedcase sample sets for the respective shifted parts, the analysisprocedure comprises causing the computer to obtain a plurality ofattribute factor analysis results by analyzing the dependency inparallel for the respective shifted parts, and the visualizationprocedure comprises causing the computer to integrate the plurality ofattribute factor analysis results, to visualize the integrated attributefactor analysis result, and to produce the visualized attribute factoranalysis result.
 20. The non-transitory computer readable recordingmedium according to claim 17, wherein the feature value of image datacomprises any selected one of feature values comprising RGB, gray scale,Laplacian, and Haar-like feature value.
 21. The non-transitory computerreadable recording medium according to claim 17, wherein the analysisprocedure comprises causing the computer to analyze the dependency byany selected one of calculating a correlation value in a sub-space ofcanonical correlation analysis, calculating mutual information, andcalculating squared-loss mutual information.
 22. The non-transitorycomputer readable recording medium according to claim 17, wherein theimage data comprises facial image data.
 23. The non-transitory computerreadable recording medium according to claim 22, wherein the attributedata comprises data representing an impression of a face.
 24. Thenon-transitory computer readable recording medium according to claim 23,wherein the impression comprises at least one selected from amongimpressions comprising “cheerfulness and friendliness”, “prettiness”,“business appropriateness”, “kindness”, and “healthy appearance”.